Adaptive Least Squares Support Vector Machine Predictor for Blast Furnace Ironmaking Process

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Least Squares Support Vector Machine for Constitutive Modeling of Clay

Constitutive modeling of clay is an important research in geotechnical engineering. It is difficult to use precise mathematical expressions to approximate stress-strain relationship of clay. Artificial neural network (ANN) and support vector machine (SVM) have been successfully used in constitutive modeling of clay. However, generalization ability of ANN has some limitations, and application of...

متن کامل

Improving the Solution of Least Squares Support Vector Machines with Application to a Blast Furnace System

The solution of least squares support vector machines LS-SVMs is characterized by a specific linear system, that is, a saddle point system. Approaches for its numerical solutions such as conjugate methods Sykens and Vandewalle 1999 and null space methods Chu et al. 2005 have been proposed. To speed up the solution of LS-SVM, this paper employs the minimal residual MINRES method to solve the abo...

متن کامل

Neural Modeling as a Tool to Support Blast Furnace Ironmaking

This paper describes the development of a hybrid model based on artificial neural network and its industrial application to the ironmaking at Companhia Siderúrgica Nacional (CSN -Volta Redonda/Brazil). The Iron Blast Furnace is highly complex process subject to oscillations in raw material characteristics. A precise model is essential to adjust © 2002 charging and blow conditions to match produ...

متن کامل

Sparse least squares Support Vector Machine classifiers

In least squares support vector machine (LS-SVM) classi-ers the original SVM formulation of Vapnik is modiied by considering equalit y constraints within a form of ridge regression instead of inequality constraints. As a result the solution follows from solving a set of linear equations instead of a quadratic programming problem. Ho wever, a d r a wback is that sparseness is lost in the LS-SVM ...

متن کامل

Sparse Least Squares Support Vector Machine Classiiers

In least squares support vector machine (LS-SVM) classi-ers the original SVM formulation of Vapnik is modiied by considering equality constraints within a form of ridge regression instead of inequality constraints. As a result the solution follows from solving a set of linear equations instead of a quadratic programming problem. However, a drawback is that sparseness is lost in the LS-SVM case ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: ISIJ International

سال: 2015

ISSN: 0915-1559,1347-5460

DOI: 10.2355/isijinternational.55.845